We know what you want to buy: a demographic-based system for product recommendation on microblogs

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Product recommender systems are often deployed by e-commerce websites to improve user experience and increase sales. However, recommendation is limited by the product information hosted in those e-commerce sites and is only triggered when users are performing e-commerce activities. In this paper, we develop a novel product recommender system called METIS, a MErchanT Intelligence recommender System, which detects users' purchase intents from their microblogs in near real-time and makes product recommendation based on matching the users' demographic information extracted from their public profiles with product demographics learned from microblogs and online reviews. METIS distinguishes itself from traditional product recommender systems in the following aspects: 1) METIS was developed based on a microblogging service platform. As such, it is not limited by the information available in any specific e-commerce website. In addition, METIS is able to track users' purchase intents in near real-time and make recommendations accordingly. 2) In METIS, product recommendation is framed as a learning to rank problem. Users' characteristics extracted from their public profiles in microblogs and products' demographics learned from both online product reviews and microblogs are fed into learning to rank algorithms for product recommendation. We have evaluated our system in a large dataset crawled from Sina Weibo. The experimental results have verified the feasibility and effectiveness of our system. We have also made a demo version of our system publicly available and have implemented a live system which allows registered users to receive recommendations in real time.



Publication date4 Aug 2014
Publication titleKDD '14 : proceedings of the 20th ACM SIGKDD international conference on Knowledge Discovery and Data mining
Place of PublicationNew York, NY (US)
Number of pages10
ISBN (Print)978-1-4503-2956-9
Original languageEnglish
Event20th ACM SIGKDD international conference on Knowledge Discovery and Data mining - New York, NY, United States
Duration: 24 Aug 201427 Aug 2014


Conference20th ACM SIGKDD international conference on Knowledge Discovery and Data mining
Abbreviated titleKDD 2014
CountryUnited States
CityNew York, NY

Bibliographic note

Funding: EPSRC (grant EP/L010690/1); National Key Basic Research Program (973 Program) of China under grant No. 2014CB340403, 2014CB340405, NSFC Grant 6127234


  • e-commerce, microblog, product demographic, product recommender


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